Research
My primary research areas include computational neuroscience, mathematical statistics, data analysis, and deep learning. Currently, my work is focused on inferring connections in small to medium-sized neuronal network models, analyzing neural data recordings, developing novel generators for statistical distributions, devising estimation techniques for statistical distribution parameters, and exploring the integration of deep learning into neural network applications.
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For more information about our research and team, please read the CSDA (Computational Statistics and Data Analytics) Lab: https://csdalab.github.io/
Highlights
Computational
Neuroscience
Analysis for Neuron Recordings
Statistic Distributions
Neural Network
Connectivity
Deep Learning and Neural Network Models
Selected Publications
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Pu, S., Dang, W., Qi, X. L., & Constantinidis, C. (2024). Prefrontal neuronal dynamics in the absence of task execution. Nature Communications, 15(1), 6694.
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Chen, X., Shi, Z., Xie, Y., Zhang, Z., Cohen, A., & Pu, S. (2024). Advancing Continuous Distribution Generation: An Exponentiated Odds Ratio Generator Approach. Entropy, 26(12), 1006.
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Yang H, Huang M, Chen X, He Z, Pu S. (2024) Enhanced Real-Life Data Modeling with the Modified Burr III Odds Ratio–G Distribution. Axioms. 13(6):401. https://doi.org/10.3390/axioms13060401
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Roy SS, Knehr H, McGurk D, Chen X, & Pu S (2024), The Lomax-Exponentiated Odds Ratio–G Distribution and Its Applications. Mathematics. 2024; 12(10):1578.
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Pu, S., Moakofi, T., & Oluyede, B. (2023). The Ristić–Balakrishnan–Topp–Leone–Gompertz-G Family of Distributions with Applications. Journal of Statistical Theory and Applications, 1-35.
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Holzhausen, K., Ramlow, L., Pu, S., Thomas, P. J., & Lindner, B. (2022). Mean-return-time phase of a stochastic oscillator provides an approximate renewal description for the associated point process. Biological Cybernetics, 116(2), 235-251.
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Dang, W., Li, S., Pu, S., Qi, X. L., & Constantinidis, C. (2022). More prominent nonlinear mixed selectivity in the dorsolateral prefrontal than posterior parietal cortex. Eneuro, 9(2).​​
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Pu, S., & Thomas, P. J. (2021). Resolving molecular contributions of ion channel noise to interspike interval variability through stochastic shielding. Biological Cybernetics, 115(3), 267-302.
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Pu, S. (2021). Noise decomposition for stochastic Hodgkin-Huxley models. Case Western Reserve University. [PhD Thesis]
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Oluyede, B., Pu, S., Makubate, B., & Qiu, Y. (2018). The gamma-Weibull-G family of distributions with applications. Austrian Journal of Statistics, 47(1), 45-76.
Posters
Recent posters have been displayed on the wall outside my office. For additional details, read the "Publications" section on the CSDA website via the following link.